Classifying Spam Emails Using Artificial Intelligent Techniques

Roy, Sanjiban Sekhar; Viswanatham, V. Madhu

doi:10.4028/www.scientific.net/jera.22.152

Cited by 20 publications

(4 citation statements)

References 13 publications

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“…Notably, the construction and saving of the classifier is aimed at the classification of incoming emails. At the final stage of classification, the incoming emails are classified by the constructed classifier into specific categories (e.g., ham, spam, phishing) [1,4].…”

Section: Problem Statementmentioning

confidence: 99%

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Survey of E-Mail Classification: Review and Open Issues

Ghaleb Abdulkadhim,

Abdulraheem Hayder

2020

ijci

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Email is an economical facet of communication, the importance of which is increasing in spite of access to other approaches, such as electronic messaging, social networks, and phone applications. The business arena depends largely on the use of email, which urges the proper management of emails due to disruptive factors such as spams, phishing emails, and multi-folder categorization. The present study aimed to review the studies regarding emails, which were published during 2016-2020, based on the problem description analysis in terms of datasets, applications areas, classification techniques, and feature sets. In addition, other areas involving email classifications were identified and comprehensively reviewed. The results indicated four email application areas, while the open issues and research directions of email classifications were implicated for further investigation.

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Section: Problem Statementmentioning

confidence: 99%

“…Figure 4 depicts the taxonomy of these features based on the corresponding email classification application arenas. The overview of these features has been presented in the following section [1,4,5].…”

Section: Feature Extraction and Feature Selectionmentioning

confidence: 99%

Survey of E-Mail Classification: Review and Open Issues

Ghaleb Abdulkadhim,

Abdulraheem Hayder

2020

ijci

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“…Through a thorough examination of these key points, one can enhance the efficacy of identifying and detecting such forms of electronic correspondence. The classification of emails into spam & non-spam categories can be achieved by the application of artificial intelligence (AI) [9]. One alternative approach to solving this problem involves extracting features from the headers, subject, & body of the messages.…”

Section: Introductionmentioning

confidence: 99%

A Novel Approach for Spam Detection Using Natural Language Processing With AMALS Models

Agarwal,

Dhoot,

Kant

et al. 2024

IEEE Access

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To enhance their company operations, organizations within the industry leverage the ecosystem of big data to manage vast volumes of information effectively. To achieve this objective, it is imperative to analyze textual data while prioritizing the safeguarding of data integrity and implementing robust measures for organizing and validating data through the utilization of spam filters. Various methodologies can be employed, including Word2Vec, bag-of-words, BERT, as well as term frequency & reciprocal document frequency (TF-IDF). Nevertheless, none of these solutions effectively address the problem of data scarcity, which might lead to the existence of missing information in the collected documents. To properly address this problem, it is necessary to employ a strategy that categorizes each document based on the topic matter and uses statistical approaches for approximation. This research paper presents a novel approach for spam detection using natural language processing. The proposed strategy utilizes a least-squares model to modify themes and incorporates gradient descent and altering least-squares (i.e., AMALS) models for estimating missing data. TF-IDF and uniform-distribution methods perform the estimation. The performance evaluation reveals that the suggested technique exhibits a superior performance of 98% compared to the existing industry TF-IDF model in accurately predicting spam within big data ecosystems. By this model, the environment of an organization or a company can be saved from spamming or other attacks, which can lead to extracting their data for unauthorized users to protect the details.

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“…(DADA et al, 2019). Outro ponto a ser destacado é que o spam acaba degradando a confiabilidade dos demais e-mails a partir do momento em que o usuário passa a apresentar dúvidas quanto à autenticidade de uma mensagem (ROY;VISWANATHAM, 2016). Por isso a necessidade de separar o spam dos demais emails se torna essencial.…”

Section: Introductionunclassified

Aplicação De Machine Learning Na Identificação De E-Mails Como Spam

Furuya

2021

COLLOQ EXACTARUM

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The e-mail service is one of the main tools used today and is an example that technology facilitates the exchange of information. On the other hand, one of the biggest obstacles faced by e-mail services is spam, the name given to the unsolicited message received by a user. The machine learning application has been gaining prominence in recent years as an alternative for efficient identification of spam. In this area, different algorithms can be evaluated to identify which one has the best performance. The aim of the study is to identify the ability of machine learning algorithms to correctly classify e-mails and also to identify which algorithm obtained the greatest accuracy. The database used was taken from the Kaggle platform and the data were processed bythe Orange software with four algorithms: Random Forest (RF), K-Nearest Neighbors (KNN), Support Vector Machine (SVM) and Naive Bayes (NB). The division of data in training and testing considers 80% of the data for training and 20% for testing. The results show that Random Forest was the best performing algorithm with 99% accuracy.

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Classifying Spam Emails Using Artificial Intelligent Techniques

Cited by 20 publications

References 13 publications

Survey of E-Mail Classification: Review and Open Issues

Survey of E-Mail Classification: Review and Open Issues

A Novel Approach for Spam Detection Using Natural Language Processing With AMALS Models

Aplicação De Machine Learning Na Identificação De E-Mails Como Spam

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